ORGLIST SYMPOSIUM 2007"Computers at the frontiers of Organic Chemistry"

Context

ORGLIST is the international mailing list of Organic Chemistry on the
Internet, founded in March 1997. Since then, thousands of chemists from all over the world have gathered
in ORGLIST to discuss Organic Chemistry. The full archive is available at the web site
and is indexed by Google. Useful content, as well as a complex thread
of personal relationships,
emerged from the interaction of the members through this simple
technological
framework. The 10 years of ORGLIST in 2007 were celebrated
scientifically, with a symposium on "Computers at the frontiers of
Organic Chemistry" at the 7th National Meeting of Organic Chemistry of the Portuguese Chemical Society, on July 17, 2007.

Both chirality and
aromaticity are cornerstone concepts for organic chemistry. Both had their
origins in the 1840s or thereafter in the work of Pasteur,
van't Hoff and LeBel for the former and Faraday, Loschmidt, Kekule,
Armstrong for the latter, this reaching its first stage of theoretical maturity
with Huckel's quantum mechanical analysis in the 20th Century (the
famous 4n+2 rule).

For a long period, these
two concepts were thought to be exclusive; after all aromaticity manifested
almost entirely in flat (achiral) benzenoid rings!

Another concept, topology,
also originated in the 1840s, having been coined
by the mathematician Johann Listing, who also proposed fascinating
topological objects such as trefoil knots, and rings now better known by their co-discoverer,
Mobius. In the 1960s, the concepts of Mobius topologies and
aromaticity started merging. The chemist Heilbronner proposed aromaticity
rules for Mobius cycles, although he did not identify such cycles as
being chiral (this property appears to have been gradually realised only years
later, although its difficult to find this expressed in print). The first
such Mobius molecule was only synthesized in 2003; it was not however particularly
aromatic! Meanwhile, in 1978 molecular biologists had discovered the fascinating
twists and knots in cyclic DNA, via James Wang's topoisomerase
enzymes. This was expressed using a concept known as supercoiling, and a new
generation of mathematicians formalised this into an equation expressing a
so-called linking number, which is comprised of twist and writhe;

Lk = T +
W ...(1)

Applied extensively
to the properties of cyclic DNA, these concepts did not migrate at all
to organic chemists, who by and large dealt with much smaller molecules.
Listing in 1847 had also introduced the concept of paradromic winding, which in
modern language maps to imparting further twists to the basic Mobius topology.
In 2005, we fused these various concepts from chemistry, topology and molecular
biology, recognising that a new form of aromaticity based on double- and higher
twisted conjugated, and importantly chiral, rings could be possible. We
identified various interesting candidate molecules, but were surprised by how
relatively stable they appeared (by computation), given they were at least
twice as twisted as the classical Mobius rings. We found a resolution to
this paradox in equation (1). The (quantum mechanical)
instability we realised is associated with T and not with W. We have
now computed values of T and W for a range of topologically
interesting (and chiral!) systems, and approximately, those that appear the
most synthetically interesting have large values of W compared
to T. So W (the writhe) can be regarded as a
fundamentally new property of cyclic conjugated molecules, and one moreover
that might be associated with stability. This has led to our proposal
that eqn (1) and the Huckel 4n+2 rule can be combined as follows;
If Lk is even (measured in units of pi), aromaticity is implied for 4n+2
cyclic conjugated electrons ... (2)

Intriguingly both T
and W are chiral indices, and they can act together or oppose to create
some fascinating novel chiral isomerisms.

In a general sense,
this type of aromaticity is chiral, and benzene like systems are very much the
achiral exceptions (having Lk = 0).

At the end of the
talk, I will speculate on some potential real world applications of this
fascinating new form of chiral aromaticity, particularly to the design of new
chiral metal ligands, and perhaps even mention another interest of ours, the
Semantic Web, and how this might in the future enable more efficient fusion of
diverse ideas and concepts (linking is a fundamental concept there as well!).

Computational models to aid
safety-directed drug design

Scott Boyer

Senior Principal Scientist
and Head, Computational Toxicology

Global Safety Assessment,
AstraZeneca R&D, Mölndal, Sweden

Access to metabolism and
toxicology data is critical to effective decision making in early drug
discovery projects.Often in such
projects little is known about the therapeutic target and usually even less is
known about potential metabolism or adverse effects of the chemical series
being investigated.Simply providing
unstructured metabolism- and safety-related information on targets and chemical
series to project teams trying to make decisions is not adequate due to the
varied nature and quality of metabolism and toxicology data.This presentation gives examples of how
relevant data can be structured, mined and in some cases modelled to enhance
decision-making.Project examples will
be presented of QSAR models and their interpretation, including
characterization of the underlying assay error for better interpretation of the
model results, development of SAR systems that support decision-making and
enhance awareness around such endpoints as metabolism/P450 activation,
mutagenesis, hERG and reactive intermediates.In general, metabolism and toxicology data should be structured
depending on, 1) its intended use, 2) its overall quality and 3) its internal
data structure (text vs. numerical) to assure its optimum use.Brief examples of the varying data types and
their usage in project decision making will be presented along with some
strategies for hypothesis generation around adverse events using a combined
approach of molecular modelling/virtual screening and text mining.Together, these tools, built to be
appropriate to the various data types, represent a basic toolkit for the
toxicologist and drug metabolism scientist needing to make meaningful
contributions to the myriad decisions made in early drug discovery projects.

Deriving
Structure-Activity Relationships in Heterogeneous Datasets

Machine learning algorithms
such as Binary Kernel Discrimination and Support Vector Machines have become
popular methods for the analysis of high-throughput screening data. While they
have been shown to be effective ways of deriving predictive models they suffer
from the disadvantage that the models are not easily interpretable. Here we
describe a new method based on genetic programming. A training set of active
and inactive molecules are represented as reduced graphs and genetic
programming is used to evolve reduced graph queries (subgraphs) that are best
able to separate the actives from the inactives. The classification rate is
determined using the F-measure which combines recall and precision into a
single objective. The resulting queries are validated on datasets not used in
deriving the queries, for proof of their predictive power.
As well as being useful models for prediction, the queries contain interpretable
structure-activity information encoded within the reduced graph nodes. Results
are presented for the well known MDDR dataset and also for GSK in-house
screening data.

This work
presents combined theoretical and experimental studies [1,2] of the
regioselectivity of O-methylation of
nitrocatechol-type inhibitors of the enzyme Catechol-O-methyltransferase (COMT).

As a case study, two simple regioisomeric
nitrocatechol-type inhibitors of COMT, containing a benzoyl substituent
attached at the meta- or at the ortho-position, respectively, relative
to the nitro group, were studied with regards to their interaction with the
catalytic site of the enzyme and the in
vitro regioselective formation of their mono-O-methyl ether metabolites. It is shown that the particular
substitution pattern of the classical nitrocatechol pharmacophore has a
profound impact on the regioselectivity of O-methylation.

In order to provide a plausible
interpretation of these results, a comprehensive analysis of the
protein-inhibitor interactions and of the relative chemical susceptibility to O-methylation of the catechol hydroxyl
groups was performed by means of docking simulations and molecular orbital
calculations. The major structural and chemical factors that determine the
enzyme regioselectivity of O-methylation
are identified and the X-ray structure of the complex of COMT with one of the
two inhibitors (BIA 8-176) is disclosed. This is the first reported structure
of COMT complexed with a nitrocatecholic inhibitor having a bulky substituent
group in ortho position to the nitro
group. Structural and dynamic aspects of this complex are analyzed and
discussed, in the context of the present study.

This work was partly funded by Fundação para a Ciência e
Technologia/AdI trough research projects POCTI/COMT-HUM/2002 and
POCTI/BME/38306/2001 and grant SFRH/BD/5228/2001 (M.L.R)

From MestReC to Mnova: A
revolutionary Approach to NMR

Nikolay Larin, Stan Sykora,
Santiago Domínguez, Carlos Cobas

MESTRELAB RESEARCH, Santiago de
Compostela, Spain

High Resolution NMR spectroscopy is undoubtedly one of
the most important methods used in organic chemistry for structure
determination. Traditionally, organic chemists used to spend considerable time
processing their NMR data to get the best experimental NMR as starting material
for the lengthy and non trivial task of spectral analysis. Furthermore, recent
years have witnessed dramatic improvements in high-throughput NMR in such a way
that spectral processing and analysis have emerged as a new bottle neck due to
the large amount ofspectral data
available.

In
this work we present Mnova, the new
incarnation of MestReC as a novel software solution offering an innovative
paradigm for the unattended NMR data processing and new tools such as spectral
prediction, simulation and fitting algorithms to facilitate structure
verification and elucidation for organic chemists.

Two-Parameter
Classifier for Prediction of PKC-ζ Modulating Behaviour of Xanthones

Protein kinase C ζ (PKC-ζ) occurs in many tissues in the
body and is associated with numerous cellular processes including
differentiation, mitogenesis, migration and apoptosis. PKC-ζ is implicated in
the progression of a variety of disease states including colon cancer,
inflammatory bowel conditions, leukaemia, melanoma and T-cell mediated
hepatitis. Studies in our research group [1, 2] have identified a number of
simple xanthone derivatives displaying varying levels and types of PKC- ζ
modulating activity. Although structurally very similar, this group of
compounds includes both potent activators and inhibitors of PKC-ζ and therefore
it is desirable to have a method with which to attempt to predict which region
of the activity spectrum new derivatives might fall into.

In an attempt to rationalize the behaviour of these
compounds a computational QSAR study was undertaken and a two-parameter decision
tree developed that successfully classifies all of the xanthones previously
tested as either activators, inhibitors or inactive. In addition, a small selection
of non-xanthone PKC-ζ inhibitors have been appended to this study and these are
also correctly classified by the decision tree developed for the xanthones.